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Studies in the Climate Dynamics of Moist Process Variability and Change
项目编号1936810
J. David Neelin
项目主持机构University of California-Los Angeles
开始日期2020-03-15
结束日期02/29/2024
英文摘要Simulations of climate change show increases in the frequency of occurrence of extreme precipitation as temperature rises, in general agreement with observations. Increases in extremes are to be expected given that warmer air typically contains more moisture. But the moisture-temperature relationship does not by itself provide a theory to predict increases in the frequency and severity of extreme precipitation in a warming climate. Moreover, a satisfactory theoretical framework would address changes in the frequency of occurrence of precipitation events of all magnitudes, from weak to moderate to unprecedented.

Work under this award seeks to develop such a theoretical framework by treating convective precipitation as a random process and applying the tools of statistical mechanics (the emphasis on convective precipitation is reasonable given that convective clouds are the source of most intense precipitation). The goal is to understand how the probability of occurrence of convective precipitation events of a given magnitude or intensity, referred to as the probability distribution, depends on temperature, moisture, and other environmental factors. Earlier work by the PI and others shows initiation and rapid intensification in convective precipitation as atmospheric moisture increases beyond a temperature-dependent threshold, a sort of threshold value of bulk column relative humidity.

Research supported here extends earlier results by incorporating considerations of thermodynamic energy, atmospheric stability, and the probability distribution of the column moisture responsible for threshold behavior in precipitation. One novelty of the work is the use of collocated data from the Global Precipitation Mission and Radio Occultation receivers on the COSMIC and COSMIC-2 satellites to explore the high moisture/heavy precipitation regime. Additional work uses these ideas to develop representations of convective precipitation for use in models of the interaction of precipitation and atmospheric circulation. Results of the research are used to evaluate simulations from state-of-the-art climate models, attempting to connect extreme precipitation behaviors in present-day simulations to future increases. If robust connections can be found the present-day simulations can be evaluated against relevant observations, and the success of models in reproducing observed connections can be used as a benchmark to assess the validity of their simulations of the response to anthropogenic forcing.

The project has societal relevance due to the connection between warming temperatures and increases in extreme event frequency and severity. Concerns over increasing damage from extreme precipitation have prompted considerable effort in generating and analyzing future climate simulations from global climate models and higher resolution regional models. But results of this "brute force" approach come with substantial uncertainty regarding the degree, geographical distribution, and other characteristics of the increases. A solid theoretical framework would thus be quite valuable in guiding the effort and interpreting its results. This project makes a direct connection between the development of new theory and its application to the analysis of climate model behaviors, thereby establishing an immediate connection to the larger enterprise of climate change impacts assessment. In addition, the project provides support and training for a graduate student, and results of the work are used to develop classroom teaching materials.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
资助机构US-NSF
项目经费$1,112,585.00
项目类型Standard Grant
国家US
语种英语
文献类型项目
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/210809
推荐引用方式
GB/T 7714
J. David Neelin.Studies in the Climate Dynamics of Moist Process Variability and Change.2020.
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